Department of General Practice, University of Tampere, Finland.
Scand J Prim Health Care. 2010 Mar;28(1):55-61. doi: 10.3109/02813431003690596.
The aim of this study was to examine risk factors that predict persistent healthcare frequent attendance among a frequent attender (FA) population.
Prospective cohort study without intervention.
Primary healthcare centre in Tampere, Finland.
A total of 85 primary healthcare working-age patients participated in the study. All participants were FAs in the first study year.
We identified two groups of patients: temporary FAs and persistent FAs. A patient was considered as a persistent FA if he or she visited the health centre at least eight times a year for at least three out of four follow-up years. Some 59 different variables were examined as potential risk factors for persistent FA. P-course, a web-based Naïve Bayesian classification tool, was used for the modelling of the data.
In our model, the most influential predictive risk factors for persistent frequent attendance in an FA population were female gender, body mass index above 30, former frequent attendance, fear of death, alcohol abstinence, low patient satisfaction, and irritable bowel syndrome. New observations were high body mass index, alcohol abstinence, irritable bowel syndrome, low patient satisfaction, and fear of death.
In FA analyses, distinction between temporary and persistent frequent attendance should be made. Our Bayesian model could be used for identifying persistent FAs in uncertain situations. The model can quite easily be further developed as a practical decision support tool for general practitioners. However, before its use in practice, the external validity of the model will need to be defined.
本研究旨在探讨预测高就诊频率人群中持续高就诊率的相关因素。
无干预的前瞻性队列研究。
芬兰坦佩雷市的一个初级保健中心。
共 85 名参加研究的初级保健工作年龄患者。所有参与者均为第一年研究中的高就诊频率患者。
我们确定了两组患者:临时高就诊频率患者和持续高就诊频率患者。如果患者在至少 4 年的随访中每年至少就诊 8 次且至少 3 次,那么他被认为是持续高就诊频率患者。共检查了 59 个不同的变量作为持续高就诊频率的潜在危险因素。使用基于网络的 Naive Bayes 分类工具 P-course 对数据进行建模。
在我们的模型中,预测高就诊频率人群中持续高就诊率的最具影响力的预测风险因素是女性、BMI 指数超过 30、曾有高就诊频率、害怕死亡、戒酒、低患者满意度和肠易激综合征。新观察到的因素是高 BMI 指数、戒酒、肠易激综合征、低患者满意度和害怕死亡。
在高就诊频率分析中,应区分临时和持续高就诊频率。我们的贝叶斯模型可用于在不确定情况下识别持续高就诊频率的患者。该模型可以很容易地进一步开发为普通科医生的实用决策支持工具。然而,在实际应用之前,需要确定模型的外部有效性。